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Upload merged model (base + LoRA adapter)

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README.md CHANGED
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  ---
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- base_model: unsloth/Qwen3-4B-Instruct-2507
 
 
 
 
 
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  library_name: peft
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  pipeline_tag: text-generation
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  tags:
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- - base_model:adapter:unsloth/Qwen3-4B-Instruct-2507
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  - lora
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- - transformers
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- - unsloth
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  ---
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- # Model Card for Model ID
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- <!-- Provide a quick summary of what the model is/does. -->
 
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- ## Model Details
 
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- ### Model Description
 
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- <!-- Provide a longer summary of what this model is. -->
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- - **Developed by:** [More Information Needed]
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- - **Funded by [optional]:** [More Information Needed]
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- - **Shared by [optional]:** [More Information Needed]
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- - **Model type:** [More Information Needed]
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- - **Language(s) (NLP):** [More Information Needed]
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- - **License:** [More Information Needed]
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- - **Finetuned from model [optional]:** [More Information Needed]
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- ### Model Sources [optional]
 
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- ## Uses
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- <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
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-
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- ### Direct Use
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- <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
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- [More Information Needed]
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-
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- ### Downstream Use [optional]
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- <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
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- [More Information Needed]
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- ### Out-of-Scope Use
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- <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
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- [More Information Needed]
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-
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- ## Bias, Risks, and Limitations
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- <!-- This section is meant to convey both technical and sociotechnical limitations. -->
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- [More Information Needed]
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-
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- ### Recommendations
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- <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
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- Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
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-
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- ## How to Get Started with the Model
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- Use the code below to get started with the model.
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- [More Information Needed]
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-
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- ## Training Details
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-
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- ### Training Data
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- <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
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- [More Information Needed]
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- ### Training Procedure
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-
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- <!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
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-
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- #### Preprocessing [optional]
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- [More Information Needed]
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- #### Training Hyperparameters
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- - **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
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-
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- #### Speeds, Sizes, Times [optional]
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- <!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
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- [More Information Needed]
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-
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- ## Evaluation
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- <!-- This section describes the evaluation protocols and provides the results. -->
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- ### Testing Data, Factors & Metrics
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- #### Testing Data
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- <!-- This should link to a Dataset Card if possible. -->
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- [More Information Needed]
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- #### Factors
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- <!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
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- [More Information Needed]
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- #### Metrics
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- <!-- These are the evaluation metrics being used, ideally with a description of why. -->
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- ### Results
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- #### Summary
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- ## Model Examination [optional]
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- <!-- Relevant interpretability work for the model goes here -->
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- ## Environmental Impact
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- <!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
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- Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
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- - **Hardware Type:** [More Information Needed]
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- - **Cloud Provider:** [More Information Needed]
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- - **Carbon Emitted:** [More Information Needed]
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- ## Technical Specifications [optional]
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- ### Model Architecture and Objective
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- [More Information Needed]
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- ### Compute Infrastructure
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- #### Hardware
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- #### Software
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- ## Citation [optional]
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- ## Glossary [optional]
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- [More Information Needed]
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- ## More Information [optional]
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- ## Model Card Authors [optional]
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- ## Model Card Contact
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- [More Information Needed]
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- ### Framework versions
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- - PEFT 0.18.1
 
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  ---
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+ base_model: Qwen/Qwen3-4B-Instruct-2507
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+ datasets:
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+ - 'u-10bei/structured_data_with_cot_dataset_512_v2'
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+ language:
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+ - en
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+ license: apache-2.0
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  library_name: peft
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  pipeline_tag: text-generation
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  tags:
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+ - qlora
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  - lora
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+ - structured-output
 
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  ---
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+ # test-v0
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+ This repository provides a **LoRA adapter** fine-tuned from
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+ **Qwen/Qwen3-4B-Instruct-2507** using **QLoRA (4-bit, Unsloth)**.
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+ This repository contains **LoRA adapter weights only**.
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+ The base model must be loaded separately.
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+ ## Training Objective
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+ This adapter is trained to improve **structured output accuracy**
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+ (JSON / YAML / XML / TOML / CSV).
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+ Loss is applied only to the final assistant output,
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+ while intermediate reasoning (Chain-of-Thought) is masked.
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+ ## Training Configuration
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+ - Base model: Qwen/Qwen3-4B-Instruct-2507
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+ - Method: QLoRA (4-bit)
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+ - Max sequence length: 2048
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+ - Epochs: 2
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+ - Learning rate: 1e-06
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+ - LoRA: r=64, alpha=128
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+ ## Usage
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+ ```python
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+ from transformers import AutoModelForCausalLM, AutoTokenizer
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+ from peft import PeftModel
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+ import torch
 
 
 
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+ base = "Qwen/Qwen3-4B-Instruct-2507"
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+ adapter = "amu870/test-v0"
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+ tokenizer = AutoTokenizer.from_pretrained(base)
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+ model = AutoModelForCausalLM.from_pretrained(
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+ base,
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+ torch_dtype=torch.float16,
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+ device_map="auto",
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+ )
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+ model = PeftModel.from_pretrained(model, adapter)
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+ ```
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+ ## Sources & Terms (IMPORTANT)
 
 
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+ Training data: ['u-10bei/structured_data_with_cot_dataset_512_v2']
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+ Dataset License: MIT License. This dataset is used and distributed under the terms of the MIT License.
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+ Compliance: Users must comply with the MIT license (including copyright notice) and the base model's original terms of use.
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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